Efficient Recursive Data-enabled Predictive Control (Extended Version)
In the field of model predictive control, Data-enabled Predictive Control (DeePC) offers direct predictive control, bypassing traditional modeling. However, challenges emerge with increased computational demand due to recursive data updates. This paper introduces a novel recursive updating algorithm for DeePC. It emphasizes the use of Singular Value Decomposition (SVD) for efficient low-dimensional transformations of DeePC in its general form, as well as a fast SVD update scheme. Importantly, our proposed algorithm is highly flexible due to its reliance on the general form of DeePC, which is demonstrated to encompass various data-driven methods that utilize Pseudoinverse and Hankel matrices. This is exemplified through a comparison to Subspace Predictive Control, where the algorithm achieves asymptotically consistent prediction for stochastic linear time-invariant systems. Our proposed methodologies' efficacy is validated through simulation studies.
Code (0)
등록된 구현이 없습니다.
Tasks
FormModel Predictive ControlSimilar Papers 제목 키워드 기반
Data-Driven Robust Backward Reachable Sets for Set-Theoretic Model Predictive Control
In this paper, we propose a novel approach for computing robust backward reachable sets from noisy data for unknown constrained linear systems subject to bounded disturbances. In particular, we develop an algorithm for o…
Model Predictive ControlNetwork-Realized Model Predictive Control Part I: NRF-Enabled Closed-loop Decomposition
A two-layer control architecture is proposed, which promotes scalable implementations for model predictive controllers. The top layer acts as both reference governor for the bottom layer, and as a feedback controller for…
Model Predictive ControlRobust constrained nonlinear Model Predictive Control with Gated Recurrent Unit model -- Extended version
In this paper we propose a robust Model Predictive Control where a Gated Recurrent Unit network model is used to learn the input-output dynamic of the system under control. Robust satisfaction of input and output constra…
modelModel Predictive ControlPrivacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the …
Privacy PreservingRecursively feasible stochastic predictive control using an interpolating initial state constraint -- extended version
We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances. State of the art SMPC approaches with closed-loop chance constraint satisfaction recursivel…
Model Predictive Control